Hyperspectral data classification using RVM with pre-segmentation and RANSAC

Abstract

Relevance vector machines (RVMs) and support vector machines (SVMs) are known to outperform classical supervised classification algorithms. RVMs have some advantages compared to SVMs, the most important being more sparsity. This paper presents hyperspectral image classification based on relevance vector machines with two different unsupervised segmentation methods as well as RANSAC (RANdom SAmple Consencus) applied before RVM classification. Compression is achieved using k-means or phase correlation based unsupervised segmentation, or using RANSAC cross-validation before the RVM classification step. Approximately the same hyperspectral data classification accuracy can be obtained with a smaller relevance vector rate and faster training time for the proposed pre-segmented RVM classification approach compared with direct RVM classification. The proposed approach can be used to improve the sparsity of RVM classification even further, and is particularly suitable for low-complexity applications.